Diagnosis of bacterial vaginosis

ABSTRACT

A method of diagnosing bacterial vaginosis (BV) in a female subject including: (a) obtaining an appropriate sample from the subject; and (b) detecting the presence of at least one of 2-hydroxyisovalerate (2HV) and γ-hydroxybutyrate (GHB) in the sample.

FIELD OF THE INVENTION

The field of this invention relates to markers of bacterial vaginosisand to diagnosis of bacterial vaginosis.

BACKGROUND OF THE INVENTION

The vaginal microbiota is dominated by Lactobacillus species in mostwomen, predominately by L. iners and L. crispatus (1-3). When theselactobacilli are displaced by a group of mixed anaerobes, belonging tothe genus Gardnerella, Prevotella, Atopobium and others, this increasein bacterial diversity can lead to bacterial vaginosis (BV) (1-3). BV isthe most common vaginal condition, affecting an estimated 30% of womenat any given time (4). While many women remain asymptomatic (2-5), whensigns and symptoms do arise they include an elevated vaginal pH>4.5,discharge, and malodour due to amines (6-8). BV is also associated witha number of co-morbidities, including increased transmission andacquisition of HIV and other sexually transmitted infections (9), andincreased risk of preterm labour (10).

In most instances, diagnosis is dependant upon microscopy of vaginalfluid to identify BV-like bacteria alone (Nugent Scoring (11)), or incombination with clinical signs (Amsel Criteria (12)). The precision andaccuracy of these methods are poor due to the diverse morphology ofvaginal bacteria, the observation that many women with BV areasymptomatic, and subjectivity in microscopic examination (13-15).Misdiagnosis creates stress for the patient, delays appropriateintervention and places a financial burden on the health care system. Arapid test based on stable, specific biomarkers for BV would improvediagnostic accuracy and speed, and reduce costs through improved patientmanagement.

Metabolomics, defined as the complete set of small molecules in a givenenvironment, has been utilized in a variety of systems to identifybiomarkers of disease (16,17), and provide functional insight intoshifts in microbial communities (18).

A rapid test based on a well characterized consistent marker forbacterial vaginosis would improve diagnostic accuracy of BV and decreasetime required for diagnosis and treatment, potentially preventingpreterm labour and other complications including acquisition of sexuallytransmitted infections.

SUMMARY OF THE INVENTION

The present invention relates to novel markers of bacterial vaginosisand to diagnosis of bacterial vaginosis.

In one embodiment, the present invention is a method for detecting thepresence of bacterial vaginosis (BV) in a female subject. The method, inone embodiment, includes: (a) obtaining a sample from the vaginal regionof the subject; (b) detecting at least one of 2-hydroxyisovalerate andγ-hydroxybutyrate (GHB) present in the sample, wherein the detection ofat least one of 2-hydroxyisovalerate and γ-hydroxybutyrate (GHB)indicates the presence of bacterial vaginosis in the subject.

In one embodiment of the method of the present invention, the methodfurther comprises the step of correlating the presence of detected2-hydroxyisovalerate and/or γ-hydroxybutyrate (GHB) with the presence ofbacterial diversity.

The present invention relates to the use metabolomic profiling orindividual metabolites as biomarkers to diagnose BV.

In one embodiment, the present invention is a method of diagnosingbacterial vaginosis (BV) in a female subject comprising: (a) obtainingan appropriate sample from the subject; and (b) detecting the presenceof at least one of 2-hydroxyisovalerate (2HV) and γ-hydroxybutyrate(GHB) in the sample, wherein the presence of at least one of 2HV and GHBindicates BV diagnosis in the subject.

In one embodiment of the method of diagnosing BV of the presentinvention, the ratio of at least one of 2HV and GHB to anothermetabolite in the sample is calculated, and wherein said diagnosis isbased on the ratio.

In another embodiment of the method of diagnosing BV of the presentinvention, the presence of at least one of 2HV and GHB is detected usinghigh performance liquid chromatography, thin layer chromatography (TLC),electrochemical analysis, Mass Spectroscopy (MS), refractive indexspectroscopy (RI), Ultra-Violet spectroscopy (UV), fluorescent analysis,radiochemical analysis, Near-InfraRed spectroscopy (Near-IR), NuclearMagnetic Resonance spectroscopy (NMR), fluorescence spectroscopy, dualpolarisation interferometry, computational methods, Light Scatteringanalysis (LS), gas chromatography (GC), or GC coupled with MS (GC-MS),direct injection (DI) coupled with LC-MS/MS.

In another embodiment of the method of diagnosing BV of the presentinvention, the presence of 2HV or GHB is detected using GC-MS, the ratioof at least one of 2HV and GHB to tyrosine in the sample is calculated,and wherein said diagnosis is based on the ratio.

In another embodiment of the method of diagnosing BV of the presentinvention, the ratio of GHB to tyrosine is 0.6 or above for BVdiagnosis.

In another embodiment of the method of diagnosing BV of the presentinvention, the ratio of 2-HV to tyrosine is 0.8 or above for BVdiagnosis.

In another embodiment of the method of diagnosing BV of the presentinvention, the method does not rely on detecting or measuring thepresence of succinate in the sample.

In another embodiment, the present invention is a method of diagnosingbacterial vaginosis (BV) in a female subject including: (a) obtaining anappropriate sample from the subject; and (b) obtaining a level of atleast one metabolite in the sample and comparing the level of the atleast one metabolite in the sample to the level of said at least onemetabolite in a known normal sample (control sample), wherein thepresence of the at least one metabolite in relatively higher levels thanin the normal sample indicates BV diagnosis in the subject, and whereinthe metabolite is selected from the group consisting of2-hydroxyisovalerate (2HV), γ-hydroxybutyrate (GHB), methyl phosphate,2-hydroxyglutarate, 5-aminovalerate, 2-hydroxyisocaproate,2-hydroxy-3-methylvalerate, mannose-6-phosphate,2-O-glycerol-d-galactopyranoside, beta-alanine, phenylethylamine andn-acetyl-putrescine.

In one embodiment of the previous method, a ratio of the level of the atleast one metabolite to the level of another metabolite in the sample iscalculated, and the diagnosis is based on the ratio.

In another embodiment of the method of diagnosing BV, the level of theat least one metabolite is detected using high performance liquidchromatography, thin layer chromatography (TLC), electrochemicalanalysis, Mass Spectroscopy (MS), refractive index spectroscopy (RI),Ultra-Violet spectroscopy (UV), fluorescent analysis, radiochemicalanalysis, Near-InfraRed spectroscopy (Near-IR), Nuclear MagneticResonance spectroscopy (NMR), fluorescence spectroscopy, dualpolarisation interferometry, computational methods, Light Scatteringanalysis (LS), gas chromatography (GC), or GC coupled with MS (GC-MS),direct injection (DI) coupled with LC-MS/MS.

In another embodiment of the method of diagnosing BV, the method doesnot rely on obtaining the level of succinate in the sample.

In one embodiment of the method of any of the previous embodiments, thesample is a sample of vaginal fluid.

In one embodiment of the method according to any of the previousembodiments, the method further includes specific pathogenic bacterialquantification. In one aspect, the specific pathogenic bacteria isselected from the group consisting of G. vaginalis and bacteria of thegenera Dialister, Prevotella and Atopobium.

The present invention, in another embodiment, provides for a method oftreating bacterial vaginosis (BV) in a patient, the method includingobtaining a metabolic profile of the patient, correlating eachmetabolite of the metabolic profile with a bacterium, and administeringthe patient a drug or drugs that are effective against the correlatedbacterium.

In one embodiment of the method of treating BV of the present invention,the metabolic profile includes γ-hydroxybutyrate (GHB), and wherein drugor drugs are effective against G. vaginalis.

In another embodiment of the method of treating BV of the presentinvention, the metabolic profile includes 2-hydroxyisovalerate (2HV),and wherein the drug or drugs are effective against bacteria of thegenera Dialister, Prevotella and Atopobium.

The present invention, in another embodiment, provides for a method ofdetermining the efficacy of a bacterial vaginosis (BV) treatment in apatient undergoing BV treatment, the method including: (a) obtaining anappropriate sample of the patient at different stages of the treatment;(b) obtaining the levels of at least one of 2-hydroxyisovalerate (2HV)and γ-hydroxybutyrate (GHB) in the samples, wherein a progressivedecrease in the levels of 2HV and GHB along the stages is indicative ofthe efficacy of the treatment.

In one embodiment of the method of determining the efficacy of BVtreatment of the present invention, the levels of at least one of 2HVand GHB is detected using high performance liquid chromatography, thinlayer chromatography (TLC), electrochemical analysis, Mass Spectroscopy(MS), refractive index spectroscopy (RI), Ultra-Violet spectroscopy(UV), fluorescent analysis, radiochemical analysis, Near-InfraRedspectroscopy (Near-IR), Nuclear Magnetic Resonance spectroscopy (NMR),fluorescence spectroscopy, dual polarisation interferometry,computational methods, Light Scattering analysis (LS), gaschromatography (GC), or GC coupled with MS (GC-MS), direct injection(DI) coupled with LC-MS/MS.

In one embodiment of the method of determining the efficacy of BVtreatment of the present invention, the ratio of the levels of at leastone of 2HV and GHB to the level of another metabolite in the sample iscalculated, and wherein said efficacy is determined on a decrease or anincrease in the ratio.

In one embodiment of the method of determining the efficacy of BVtreatment of the present invention, the levels of at least one of 2HVand GHB is detected using GC-MS, the other metabolite is tyrosine, andwherein said efficacy is based on the ratio of at least one of 2HV andGHB to tyrosine.

The present invention, in another embodiment, provides for a method ofdetermining the efficacy of a bacterial vaginosis (BV) treatment in apatient undergoing BV treatment, the method comprising obtaining anappropriate sample of the patient at different stages of the treatment;(b) obtaining the levels of at least one metabolite selected from thegroup consisting of 2-hydroxyisovalerate (2HV), γ-hydroxybutyrate (GHB),methyl phosphate, 2-hydroxyglutarate, 5-aminovalerate,2-hydroxyisocaproate, 2-hydroxy-3-methylvalerate, mannose-6-phosphate,2-O-glycerol-d-galactopyranoside, beta-alanine, phenylethylamine andn-acetyl-putrescine in the samples, wherein a progressive decrease inthe levels of the at least one metabolite along the different stages isindicative of the efficacy of the treatment.

In one embodiment of the previous embodiment, the levels are obtained asa ratio of the at least one metabolite to the level of anothermetabolite. In one aspect of this embodiment the other metabolite istyrosine.

The present invention, in another embodiment, provides for a method ofdiagnosing bacterial vaginosis (BV) in a subject including: (a)obtaining a metabolite profile from the subject; and (b) usingmultivariate statistical analysis and machine learning to compare thesubject's profile with a predetermined set of metabolite profiles of BVand a predetermined set of metabolite profiles of non-BV (referred to as“control” or “normal”) to determine if the subject has BV.

In one embodiment of the previous diagnostic method, the subject'smetabolite profile and the predetermined set of metabolite profiles areobtained using metabolomics.

In another embodiment of the previous diagnostic method, themetabolomics is performed with one or more of high performance liquidchromatography, thin layer chromatography, electrochemical analysis,mass spectroscopy (MS), refractive index spectroscopy, ultra-violetspectroscopy, fluorescent analysis, radiochemical analysis,near-infrared spectroscopy, nuclear magnetic resonance (NMR), lightscattering analysis, gas chromatography (GC), or GC coupled with MS,direct injection (DI) coupled with LC-MS/MS.

In another embodiment of the previous diagnostic method, the steps ofthe method are executed using a suitably programmed computer.

In another embodiment of the previous diagnostic method, metaboliteprofiles are obtained from a biological sample.

In another embodiment of the previous diagnostic method, wherein themetabolite includes at least one of 2-hydroxyisovalerate (2HV) andγ-hydroxybutyrate (GHB).

In another embodiment of the previous diagnostic method, the metaboliteinclude 2-hydroxyisovalerate (2HV), γ-hydroxybutyrate (GHB), methylphosphate, 2-hydroxyglutarate, 5-aminovalerate, 2-hydroxyisocaproate,2-hydroxy-3-methylvalerate, mannose-6-phosphate,2-O-glycerol-d-galactopyranoside, beta-alanine, phenylethylamine andn-acetyl-putrescine.

The present invention, in another embodiment, is a use of a metabolicprofile of an appropriate sample obtained from a bacterial vaginosis(BV) patient to correlate the metabolic profile of the patient withspecific bacteria, and to select a drug to treat the patient based onsaid specific bacteria.

In one embodiment according any of the previous embodiments, theappropriate sample is a sample obtained from the vaginal region,including vaginal fluids.

The present invention, in another embodiment, relates to the use of G.vaginails for the production of GHB.

BRIEF DESCRIPTION OF THE DRAWINGS

The following figures illustrate various aspects and preferred andalternative embodiments of the invention.

FIG. 1: The vaginal metabolome is most correlated with bacterialdiversity. All analyses were carried out independently for non-pregnant(left) and pregnant (right) cohorts. Row (A) Partial least squaresregression (PLS) score plot built from 128 metabolites detected by GC-MSusing bacterial diversity as a continuous latent variable. Each pointrepresents a single woman (n=131). The position of points displayssimilarities in the metabolome, with samples closest to one anotherbeing most similar. Circles are colored by diversity of the microbiotameasured using Shannon's diversity, where darker circles indicate higherdiversity. Row (B) PLS regression loadings. Each point represents asingle metabolite. Shaded circles indicate metabolites robustlyassociated with diversity in either cohort (Jackknifing, 95% CI).Shading of circles corresponds to the size of the confidence interval(CI) for each metabolite, where darker circles indicate narrower CIs.Venn diagram depicts overlap between metabolites associated withdiversity in either cohort. Cad: Cadaverine, Tya: Tyramine, Put:Putrescine, MPh: Methylphosphate, 5AV: 5-aminovalerate, HIC:2-hydroxyisocaproate, HMV: 2-hydroxy-3-methylvalerate, HV:2-hydroxyisovalerate, GHB: γ-hydroxybutyrate. Ser: serine, Asp:aspartate, Glu: glutamate, Gly: glycine, Tyr: tyrosine. NAcLys:n-acetyl-lysine, Phe: phenylalanine, Orn: ornithine.

FIG. 2: Bacterial taxa and metabolites correlated with bacterialdiversity in the vagina. Cohorts (non-pregnant and pregnant) werecombined prior to analyses. Samples are ordered by their position on thefirst component (x-axis) of a partial least squares regression (PLS)built from metabolites using bacterial diversity as a continuous latentvariable (see FIG. 8). Diversity was calculated using Shannon'sdiversity (A). The two dots indicate samples clearly misclassified byNugent. Barplots (B) display the vaginal microbiota profiled using theV6 region of the 16S rRNA gene. Each bar represents a single sample froma single woman, and each colour a different bacterial taxa. Thebacterial taxa listed on the right side of the barplots are (from top tobottom): Megasphaera, Dialister, Atopobium, Sneathia, Prevotella,Gardnerella, L. unclassified, L. jensenii, L. gasseri, L. crispatus, L.iners. (C) Nugent Score (black=7-10 (BV), dark grey=4-6 (Int), lightgrey=1-3 (N), white=ND) and pregnancy status (black=P, grey=NP). (D)Heatmap of GC-MS detected metabolites which were robustly associatedwith diversity in both cohorts (Jackknifing, 95% CI). Metabolites areclustered using average linkage hierarchical clustering. The listedmetabolites are (from top to bottom): unknown 9, glutamate *, glycine *,inositol, aspartate *, leusine *, serine *, threonine *, threose,citrate, pyrimidine, tyrosine *, urea, glycerol-3-phosphate *,phenylalanine *, unknown sugar 1, unknown sugar 2, N-acetyl-lysine,ornithine, xylulose, alpha-ketoglutarate, phosphate sugar, lysine,putrescine *, 2H3M-valerate, thymine, unknown 11, unknown sugar 4,triethanolamine, unknown 22, unknown amine 1, tyramine *, cadaverine *,methyl phosphate, GHB *, 2-hydroxyisovalerate *, 2-hydroxyisocaproate *,unknown 4, mannose-6-phosphate *, 5-aminovalerate *, unknown amine 5,n-acetyl-putrescine, unknown amine 7, phenylethylamine *, unknown amine4, unknown 2, gluconic acid, tryptophan, glucaric acid, aminomalonate,unknown 1, unknown sugar 8, phenyllactate, unknown sugar 5,glycerol-gulo-heptose, unknown amine 2. (E) Lactate and succinateabundance. From top to bottom: lactate *, succinate LC-MS *, succinateGC-MS *. (F) Color key and Histogram. Grey=ND. (*) indicates metabolitesconfirmed by authentic standards.

FIG. 3: Comparison of biomarkers to identify Nugent BV from Nugent N.(A) Odds ratios (OR) of metabolites with positive predictive value toidentify Nugent BV. Bars represent 95% Confidence Intervals. Metaboliteswere detected by GC-MS and P values generated from unpaired t-tests witha Benjamini-Hochberg correction to account for multiple testing(p<0.01). (*) indicates metabolites confirmed by authentic standards.(B) Receiver operating characteristic (ROC) curves of metabolite ratiosto identify Nugent BV from Nugent N. Ratios with largest area under thecurve (AUC) are shown, along with succinate:lactate as a comparator. (C)AUC of selected metabolite ratios to identify Nugent BV. (D) AUC ofmetabolites alone to identify Nugent BV. Panels B-D were built fromLC-MS data. GHB:γ-hyroxybutyrate, 2-HV:2-hydroxyisovalerate. [The arrowsin (B) are only to help identify the curves.]

FIG.4: Biomarker cut points effectively group Nugent Intermediatesamples as BV or N. Barplots display the vaginal microbiota of Rwandanwomen sorted by (A) GHB:tyrosine or (B) 2HV:tyrosine. Each barrepresents a single sample from a single woman and each colour adifferent bacterial taxa. Nugent scores are indicated below barplots.Black lines indicate ratio cut point for Nugent BV. Ratios werecalculated from LC-MS data. The bacterial taxa listed on the right sideof each (A) and (B) are (from top to bottom): Megasphaera, Dialister,Atopobium, Sneathia, Prevotella, Gardnerella, L. unclassified, L.jensenii, L. gasseri, L. crispatus, and L. iners.

FIG. 5: Biomarker validation in a blinded replication cohort of 45 womenfrom Tanzania. (A) BV status as defined by Nugent Score or ratio cutpoints identified in the Rwandan discovery data set. Black=BV, Gray=N.(B) Heatmap of ratio values. (C) ROC curves and (D) AUC of ratios toidentify Nugent BV from N in the validation set. 2HV:2-hydroxyisovalerate, GHB: γ-hydroxybutyrate, Tyr: tyrosine. [The arrowsin (C) are only to help identify the curves.]

FIG. 6: Graph illustrating that GHB is produced by Gardnerellavaginalis. GHB was extracted from bacteria grown on agar plates anddetected by GC-MS. Values from three independent experiments are shownwhere each point was generated from an average of technical duplicates.*p<0.05, unpaired t-test.

FIG. 7: Principal component analysis score plots. Points are coloredaccording to (A) pregnancy status, (B) the diversity of the microbiotameasured using the Shannon index, or (C) Nugent score.

FIG. 8: Combined cohort PLS regression scoreplot. Each point representsa single woman (n=131). The position of points displays similarities inthe metabolome, with samples closest to one another being most similar.Circles are colored by diversity of the microbiota measured using theShannon Index, where darker circles indicate higher diversity.

FIG. 9: Relative abundance of succinate in women dominated by L.crispatus, L. iners or Nugent BV detected by GC-MS (*) p<0.01, unpairedt-test, Benjamini-Hochberg corrected.

FIG. 10: Correlations between metabolites and taxa which are robust torandom sampling of the underlying data. P values (Benjamini-Hochbergcorrected) of Spearman's correlations are plotted on a log scale. Thesign of each p value corresponds to the directionality of thecorrelation. Only metabolites and taxa for which any p values are <0.01are displayed. List of Taxa (from top to bottom): L. iners, L.crispatus, Lactobacillus unclassified, L. gasseri/johnsonii, L.jensenii, G. vaginalis, Prefotella, Sneathia, Atopobium, Dialister,Megasphaera, Bifidobacteriaceae, Streptococcus, Leptotrichiaceae,Bifidobacterium, Anaerococcus, Parvimonas, Viellonellaceae,Fusobacterium, Peptinophilus, Desulfotomaculum, TM7, Corynebacterium,Peptostreptococcus, Enterobacgteriaceae, Leptotrichia,Clostridiales_IncertaeSedisXI, Prevotellaceae, Enterobacteriaceae,Faecalibacterium and Clostriadiales. Metabolites (from right to left):2-hydroxyisovalerate, tyramine, cadaverine, GHB, unknown amine 4,phenylethylamine, unknown amine 5, n-acetyl-putrescine, putrescine,5-aminovalerate, 2-hydroxyisocaproate, 2-hydroxyglutarate, unknown sugar1, ornithine, unknown sugar 2, threonine, serine, glutamate, glycine,proline, leucine, unknown 1, asparate, phenyllactate,glycerol-gulo-heptose, unknown sugar 9, unknown 23, lysine,N-acetyl-lysine, tyrosine, unknown 2, a_ketoglurarate, G3P,phenylalanine, pyrimidine 1, glyceric_acid, citrate, malate,Mannose-6-phosphate, succinate, unknown 17, unknown amine 7, mannonate,allose, unknown amine 8, unkown 10, n-acetyl-galactosaminitol, unknownsugar 6, unknown 21, xylopyranose, ribofuranose, 1-phenyl-1,2-ehanediol,1,2,4-butanetriol, unknown 14, propanal, unknown 8,beta-galactopyranoside, unknown 3, phosphate sugar, unknown amine 3,unknown amine 2, unknown amine 1.

DESCRIPTION OF THE INVENTION

Definitions

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Also, unless indicatedotherwise, except within the claims, the use of “or” includes “and” andvice versa. Non-limiting terms are not to be construed as limitingunless expressly stated or the context clearly indicates otherwise (forexample “including”, “having” and “comprising” typically indicate“including without limitation”). Singular forms including in the claimssuch as “a”, “an” and “the” include the plural reference unlessexpressly stated otherwise. In order to aid in the understanding andpreparation of the within invention, the following illustrative,non-limiting, examples are provided.

“Vaginal bacteria” refers to bacteria, alive or dead, that is found inthe female vagina and associated tissues and fluids.

“Metabolome” refers to the collection of all metabolites in a biologicalcell, tissue, organ or organism, which are the end products of cellularprocesses. “Metabolome” includes lipidome, sugars, nucleotides and aminoacids. Lipidome is the complete lipid profile in a biological cell,tissue, organ or organism.

“Metabolomic profiling” refers to the characterization and/ormeasurement of the small molecule metabolites in biological specimen orsample, including cells, tissue, organs, organisms, swabs, includingvaginal swabs, samples of the vaginal region, or any derivative fractionthereof and fluids such as blood, blood plasma, blood serum, saliva,synovial fluid, spinal fluids, urine, bronchoalveolar lavage, tissueextracts, sweat, vaginal fluids and so forth.

The metabolite profile may include information such as the quantityand/or type of small molecules present in the sample. The ordinarilyskilled artisan would know that the information which is necessaryand/or sufficient will vary depending on the intended use of the“metabolite profile.” For example, the “metabolite profile,” can bedetermined using a single technique for an intended use but may requirethe use of several different techniques for another intended usedepending on such factors as the disease state involved, the types ofsmall molecules present in a particular targeted cellular compartment,the cellular compartment being assayed per se., and so forth.

The relevant information in a “metabolite profile” may also varydepending on the intended use of the compiled information, e.g.spectrum. For example, for some intended uses, the amounts of aparticular metabolite or a particular class of metabolite may berelevant, but for other uses the distribution of types of metabolitesmay be relevant.

Metabolite profiles may be generated by several methods, e.g., highliquid chromatography (HPLC), thin layer chromatography (TLC),electrochemical analysis, Mass Spectroscopy (MS), refractive indexspectroscopy (RI), Ultra-Violet spectroscopy (UV), fluorescent analysis,radiochemical analysis, Near-InfraRed spectroscopy (Near-IR), NuclearMagnetic Resonance spectroscopy (NMR), fluorescence spectroscopy, dualpolarisation interferometry, computational methods, Light Scatteringanalysis (LS), gas chromatography (GC), or GC coupled with MS, directinjection (DI) coupled with LC-MS/MS and/or other methods or combinationof methods known in the art.

The term “small molecule metabolites” includes organic and inorganicmolecules which are present in the cell, cellular compartment, ororganelle, usually having a molecular weight under 2,000, or 1,500. Thesmall molecule metabolites of the cell are generally found free insolution in the cytoplasm or in other organelles, such as themitochondria, where they form a pool of intermediates which can bemetabolized further or used to generate large molecules, calledmacromolecules. The term “small molecule metabolites” includes signalingmolecules and intermediates in the chemical reactions that transformenergy derived from food into usable forms. Examples of small moleculemetabolites include phospholipids, glycerophospholipids, lipids,plasmalogens, sugars, fatty acids, amino acids, nucleotides,intermediates formed during cellular processes, isomers and other smallmolecules found within the cell. In one embodiment, the small moleculesof the invention are isolated. Preferred metabolites include lipids andfatty acids.

The term “subject” as used herein refers all members of the animalkingdom including mammals, preferably humans.

Overview

The present invention relates to the use metabolomic profiling indiagnosing and treating bacterial vaginosis (BV) in a female subject.The present invention relates also to individual or combination ofbiomarkers in diagnosing BV in a female subject.

BV is the most common vaginal condition, characterized by an increase inbacterial diversity with a corresponding decrease in Lactobacillusspecies. Clinical diagnosis often relies on microscopy, which may notreflect the microbiota composition accurately. In the present invention,novel biomarkers for BV have been identified, and demonstrate that thevaginal metabolome is strongly correlated with bacterial diversity.Furthermore, the organism responsible for producing γ-hydroxybutyrate(GHB) has been identified, and demonstrate production by this species invitro. The present invention provides a new and inventive insight intothe metabolism of the vaginal microbiota and provides for improveddetection of disease.

The inventors have identified a number of metabolites as novelbiomarkers of BV, in particular GHB and 2-hydroxyisovalerate (2HV). Thenovel biomarkers include GHB, methyl phosphate, 2-hydroxyisovalerate,2-hydroxyglutarate, 5-aminovalerate, 2-hydroxyisocaproate,2-hydroxy-3-methylvalerate, mannose-6-phosphate,2-O-glycerol-d-galactopyranoside, beta-alanine, phenylethylamine,n-acetyl-putrescine.

As such, in one embodiment the present invention provides for a methodof diagnosing BV in a subject, the method including detecting thepresence of GHB and/or 2HV in an appropriate sample of the subject. Thepresence of GHB and/or 2HV in the sample being indicative of BV in thesubject.

In another embodiment, the method of diagnosing BV in a subject mayinclude detecting the level or amount or concentration (togetherreferred to as “level”) of at least one of GHB, 2HV, cadaverine, methylphosphate, 2-hydroxyisovalerate, 2-hydroxyglutarate, putrescine,5-aminovalerate, tyramine, 2-hydroxyisocaproate,2-hydroxy-3-methylvalerate, mannose-6-phosphate,2-O-glycerol-d-galactopyranoside, beta-alanine, phenylethylamine,n-acetyl-putrescine in a sample of the subject. The obtained level(s) ofthe at least one metabolites may be compared to the level(s) of thesemetabolites in a normal sample. A higher level of the at least onemetabolites in the sample obtained from the subject relative to thenormal sample is indicative that the subject has BV.

To circumvent the need of controlling the amount of sample, such asvaginal fluid, collected, the ratio of at least one of 2-HV and GHB toanother metabolite in the sample may be calculated, for example, asratios to the amino acid tyrosine. The ratio of GHB to tyrosine of about0.6 or above may be indicative of BV diagnosis, i.e. 0.6, 0.7, 0.8, 09,1, and anything in between, such as 0.620, 0.621, 0.623, 0.624, 0.625,0.626, 0.627, 0.628, 0.629, 0.630 and so forth all the way to 1.000. Theratio of 2-HV to tyrosine of about 0.8 or above may be indicative of BVdiagnosis, i.e., 0.8, 0.9, 1 and anything in between, such as 0.800,0.801, 0.880, 0.881, 0.882, 0.883, 0.884, 0.885, 0.886, 0.887, 0.888,0.889, 0.900 and so forth all the way to 1.000.

In another embodiment, the present invention is a method of diagnosingbacterial vaginosis (BV) in a female subject. The method of thisembodiment may include: (a) obtaining an appropriate sample from thesubject; and (b) obtaining a level of at least one metabolite in thesample and comparing the level of the at least one metabolite in thesample to the level of said at least one metabolite in a known normalsample (control sample), wherein the presence of the at least onemetabolite in relatively higher levels than in the normal sampleindicates BV diagnosis in the subject, and wherein the metabolite isselected from the group consisting of 2-hydroxyisovalerate (2HV),γ-hydroxybutyrate (GHB), cadaverine, methyl phosphate,2-hydroxyglutarate, putrescine, 5-aminovalerate, tyramine,2-hydroxyisocaproate, 2-hydroxy-3-methylvalerate, mannose-6-phosphate,2-O-glycerol-d-galactopyranoside, beta-alanine, phenylethylamine andn-acetyl-putrescine.

Surprisingly, the inventors discovered that succinate is notsignificantly elevated in women with BV. As such, the method of thepresent invention may not need to (i.e. be free of) obtain the levels ofsuccinate in the sample.

The vaginal sample may be obtained from a subject by a variety of knownmethods and can be analyzed with or without processing. Typically, thesample will be obtained by swab which can then be used to directlycontact the sample with a test composition that facilitates detectingthe presence of the metabolites in the sample, e.g., by immersing theswab in a liquid containing one or more of the test reagents or byrolling or otherwise contacting the swab with the surface of a testdevice, e.g., test strip, in or on which one or more of the testreagents are disposed. Another approach involves the immersion of theswab into a processing or extraction liquid, e.g., comprising buffers,preservatives, or the like, and removal of an aliquot of supernatant,either after filtration, centrifugation, or the like to removeparticulate debris, or simply after allowing debris to settle bygravity. The aliquot of supernatant is then contacted with the testcomposition as above. In general, the method by which the sample isobtained and processed, if at all, is not critical and can be selectedaccording to the particular needs or desires of the user. Yet anotherapproach may involve a metabolome analysis of the sample. Sincemetabolites exist in a very broad range of concentrations and exhibitchemical diversity, there is no one instrument that can reliably measureall of the metabolites in the non-human or human metabolome in a singleanalysis. Instead, practitioners of metabolomic profiling generally usea suite of instruments, most often involving different combinations ofliquid chromatography (LC) or gas chromatography (GC) coupled with MS,to obtain broad metabolic coverage [Circulation. 2012; 126: 1110-1120]Although in this invention NMR and Direct Injection LC-MS/MS(DI/LC-MS/MS) metabolic profiling were used, it should be understoodthat other instruments such as electrochemical analysis, RI, UV,near-IR, LS, GC and so forth may also be used.

The metabolic approach may also facilitate a diagnosis of BV in asubject by comparing the metabolic profile of a sample obtained from thesubject, with the metabolic profile of a normal sample (“control”).

The methods of the present invention may also increase the efficacy ofthe treatment of BV. A method of treating bacterial vaginosis (BV) in apatient, may include (a) obtaining a metabolic profile of the patient,(b) correlating each metabolite of the metabolic profile with abacterium, and (c) administering the patient a drug or drugs that areeffective against the correlated bacterium.

The present invention may also provide for a method of determining theefficacy of a BV treatment. A method of determining the efficacy of abacterial vaginosis (BV) treatment in a patient undergoing BV treatmentmay include obtaining an appropriate sample of the patient at differentstages of the treatment. For example, samples may be obtained when thepatient was first diagnosed and at different time periods during thetreatment of the patient. The levels of at least one of2-hydroxyisovalerate (2HV) and γ-hydroxybutyrate (GHB) may be obtainedin the samples at each one of those different stages. The levels of 2HVand/or GHB at each stage may then be compared. A general progressivedecrease in the levels of 2HV and/or GHB along the different stages maybe indicative of the efficacy of the treatment.

A method of determining the efficacy of a bacterial vaginosis (BV)treatment in a patient undergoing BV treatment may also include (a)obtaining an appropriate sample of the patient at different stages ofthe treatment; (b) obtaining the levels of at least one metaboliteselected from the group consisting of 2-hydroxyisovalerate (2HV),γ-hydroxybutyrate (GHB), cadaverine, methyl phosphate,2-hydroxyglutarate, putrescine, 5-aminovalerate, tyramine,2-hydroxyisocaproate, 2-hydroxy-3-methylvalerate, mannose-6-phosphate,2-O-glycerol-d-galactopyranoside, beta-alanine, phenylethylamine andn-acetyl-putrescine in the samples; and comparing the levels of the atleast one metabolite in each stage. A general progressive decrease inthe levels of 2HV and GHB along the different stages may be indicativeof the efficacy of the treatment.

In addition to the previous embodiments, the present invention providesfor: using metabolomics to determine efficacy of a treatment regime forBV; detecting asymptomatic subjects with BV, particularly assessingexpecting mothers or maternal screening tests; diagnosing, prognosing ortailoring treatment of BV based on any or all of the identified BVbiomarkers; using metabolomics to identify bacterial diversity—i.e. GHBcorrelates with pathogenic G. vaginalis, 2HV correlates with Dialister,Prevotella, Atopobium; using metabolomics to identify women at risk ofpre-term birth or STD's; associating metabolomic signatures in BV withparticular bacteria to identify microbial origin of particularmetabolites.

In order to aid in the understanding and preparation of the withininvention, the following illustrative, non-limiting, examples areprovided.

EXAMPLES Example 1

Materials and Methods

Clinical Samples

Premenopausal women between the ages of 18 and 55 were recruited at theUniversity of Kigali Teaching Hospital (CHUK) and the Nyamata DistrictHospital in Rwanda. The Health Sciences Research Ethics Board at WesternUniversity, Canada, and the CHUK Ethics Committee, Rwanda grantedethical approval for the study. Participants were excluded if they hadreached menopause, had a current infection of gonorrhoea, Chlamydia,genital warts, active genital herpes lesions, active syphilis, urinarytract infections, received drug therapy that may affect the vaginalmicrobiome, had unprotected sexual intercourse within the past 48 hours,used a vaginal douche, genital deodorant or genital wipe in past 48hours, had taken any probiotic supplement in past 48 hours, or weremenstruating at time of clinical visit. After reviewing details of thestudy, participants gave their signed consent before the start of thestudy. For metabolome analysis, sterile Dacron polyester-tipped swabs(BD) were pre-cut with sterilized scissors and weighed in 1.5 mlmicrocentrifuge tubes prior to sample collection. Using sterile forcepsto clasp the pre-cut swabs, a nurse obtained vaginal samples formetabolomic analysis by rolling the swab against the mid-vaginal wall. Asecond full-length swab was obtained for Nugent Scoring and 16S rRNAgene sequencing using the same method. Nugent Scoring was performed atCHUK by Amy McMillan. Vaginal pH was measured using pH strips. Sampleswere frozen within 2 hours of collection and stored at −20° C. or belowuntil analysis.

Microbiome Profiling

Vaginal swabs for microbiome analysis were extracted using the QIAampDNA stool mini kit (Qiagen) with the following modifications: swabs werevortexed in 1 mL buffer ASL before removal of the swab and addition of200 mg of 0.1 mm zirconia/silica beads (Biospec Products). Samples weremixed vigorously for 2×30 seconds at full speed with cooling at roomtemperature between (Mini-BeadBeater; Biospec Products). After heatingto 95° C. for 5 minutes, 1.2 ml of supernatant was aliquoted into a 2mltube and one-half an inhibitEx tablet (Qiagen) was added to each sample.All other steps were performed as per the manufacturers instructions.Sample amplification for sequencing was carried out using the forwardprimer (ACACTCTTTCCCTACACGACGCTCTTCCGATCTnnnn(8)CWACGCGARGAACCT TACC)and the reverse primer(CGGTCTCGGCATTCCTGCTGAACCGCTCTTCCGATCTn(12)ACRACACGAGCTG ACG AC) wherennnn indicates four randomly incorporated nucleotides, and (8) was asample nucleotide specific barcode. The 5′ end is the adapter sequencefor the Illumina MiSeq sequencer and the sequences following the barcodeare complementary to the V6 rRNA gene region. Amplification was carriedout in 42 μL with each primer present at 0.8 pMol/mL, 20 μLGoTaq hotstart colorless master mix (Promega) and 2 μL extracted DNA. The PCRprotocol was as follows: initial activation step at 95° C. for 2 minutesand 25 cycles of 1 minute 95° C., 1 minute 55° C. and 1 minute 72° C.

All subsequent work was carried out at the London Regional GenomicsCentre (LRGC, lrgc.ca, London, Ontario, Canada). Briefly, PCR productswere quantified with a Qubit 2.0 Flourometer and the high sensitivitydsDNA specific fluorescent probes (Life Technologies). Samples weremixed at equimolar concentrations and purified with the QIAquick PCRPurification kit (QIAGEN). Samples were paired-end sequenced on anIlluminaMi-Seq with the 600 cycle version 3 reagents with 2×220 cycles.Data was extracted from only the first read, since it spanned theentirety of the V6 region including the reverse primer and barcode.

Resulting Reads were extracted and de-multiplexed using modifications ofin-house Perl and UNIX-shell scripts with operational taxonomic units(OTUs) clustered at 97% identity, similar to our reported protocol (38).Automated taxonomic assignments were carried out by examining best hitsfrom comparison the Ribosomal Database Project (rdp.cme.msu.edu) andmanually curated by comparison to the Green genes database(greengenes.lbl.gov) and an in house database of vaginal sequences(Macklaim unpublished). Taxa with matches at least 95% similarity toquery sequences were annotated as such. OTUs were summed to the genuslevel except for lactobacilli, and rare OTUs found at less than 0.5%abundance in any sample removed. Reads were deposited to the Short ReadArchive (BioProject ID: PRJNA289672). To control for backgroundcontaminating sequences, a no-template control was also sequenced.Barplots were constructed with R {r-project.org} using proportionalvalues.

To avoid inappropriate statistical inferences made from compositionaldata, centred log-ratios (clr), a method previously described byAitchison (39) and adapted to microbiome data was used with pairedt-tests for comparisons of genus and species level data (40). TheBenjamini Hochberg (False Discovery rate) method was used to control formultiple testing with a significance threshold of 0.1. All statisticalanalysis, unless otherwise indicated, was carried out using R(r-project.org).

Sample Preparation GC-MS

Vaginal swabs were pre-cut into 1.5 mL tubes and weighed prior to andafter sample collection to determine the mass of vaginal fluidcollected. After thawing, swabs were eluted in methanol-water (1:1) in1.5 mL microcentrifuge tubes to a final concentration of 50 mg vaginalfluid/mL, which corresponded to a volume ranging from 200-2696 μL,depending on the mass of vaginal fluid collected. A blank swab eluted in800 μL methanol-water was included as a negative control. All sampleswere vortexed for 10 s to extract metabolites, centrifuged for 5 min at10 621 g, vortexed again for 10 s after which time the brushes wereremoved from tubes. Samples were centrifuged a final time for 10 min at10 621 g to pellet cells and 200 μL of the supernatant was transferredto a GC-MS vial. The remaining supernatant was stored at '80° C. forLC-MS analysis. Next, 2 μL of 1 mg/mL ribitol was added to each vial asan internal standard. Samples were then dried to completeness using aSpeedVac. After drying, 100 μL of 2% methoxyamine-HCl in pyridine (MOX)was added to each vial for derivatization and incubated at 50° C. for 90min. 100 μL N-Methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) wasthen added and incubated at 50° C. for 30 min. Samples were thentransferred to micro inserts before analysis by GC-MS (Agilent 7890A GC,5975 inert MSD with triple axis detector). 1 μL of sample was injectedusing pulsed splitless mode into a 30 m DB5-MS column with 10 mduraguard, diameter 0.35 mm, thickness 0.25 μm (JNW Scientific). Heliumwas used as the carrier gas at a constant flow rate of 1 ml/min. Oventemperature was held at 70° C. for 5 min then increased at a rate of 5°C/min to 300° C. and held for 10 min. Solvent delay was set to 13 min toavoid solvent and a large lactate peak, and total run time was 61 min.Masses between 25 m/z and 600 m/z were selected by the detector. Allsamples were run in random order and a standard mix containingmetabolites expected in samples was run multiple times throughout toensure machine consistency.

Data Processing GC-MS

Chromatogram files were de-convoluted and converted to ELU format usingthe AMDIS Mass Spectrometry software (41), with the resolution set tohigh and sensitivity to medium. Chromatograms were then aligned andintegrated using Spectconnect (42) (http://spectconnect.mit.edu), withthe support threshold set to low. All metabolites found in the blankswab, or believed to have originated from derivatization reagents wereremoved from analysis at this time. After removal of swab metabolites,the IS matrix from Spectconnect was transformed using the additive logratio transformation (alr) (39) and ribitol as a normalizing agent (log2(x)/log 2(ribitol)). Zeros were replaced with two thirds the minimumdetected value on a per metabolite basis prior to transformation. Allfurther metabolite analysis was performed using these alr transformedvalues.

Metabolites were initially identified by comparison to the NIST 11standard reference database (http://www.nist. gov/srd/nist1a.cfm).Identities of metabolites of interest were then confirmed by authenticstandards if available.

Whole Metabolomic Analysis

In order to visualize trends in the metabolome as detected by GC-MS,principal component analysis (PCA) was performed using pareto scaling.To determine the percentage of variation in the metabolome that could beexplained by a single variable we performed a series of partial leastsquares (PLS) regressions where each variable was used as a continuouslatent variable. We tested every taxa, pH, Nugent score, pregnancystatus, Shannon's diversity index and sample ID and compared the percentvariation explained by the first component of each PLS. The variablewith the highest value was determined to be most closely associated withthe metabolome (Shannon's Diversity). Analysis was conducted in R usingthe PLS package and unit variance scaling. Jackknifing with 20% sampleremoval and 10 000 repetitions was then applied to determine 95%confidence intervals for each metabolite. Metabolites with confidenceintervals that did not cross zero in both cohorts (pregnant andnon-pregnant) were considered significantly associated with diversity.Heat maps of significant metabolites were constructed using theheatmap.2 function in R with average linkage hierarchical clustering andmanhattan distances. Unless specified otherwise, all tests fordifferential metabolites between groups were performed using unpairedt-test with a Benjamini-Hochberg (False Discovery Rate) significancethreshold of p<0.01 to account for multiple testing and multiple groupcomparisons. Correlations between metabolites and taxa were performedusing alr transformed values for metabolites and clr values with 128Monte Carlo instances for microbiota data in R using the ALDEx2 package(40). 16S rRNA microbial gene profiles generate compositional data thatinterferes with many standard statistical analyses, including deterdetermining correlations²⁶⁻²⁸. We used the aldex.corr function from theALDEx2 package to calculate the Spearman's rank correlation between eachOTU abundance in 128 inferred technical replicates and that weretransformed by center log-ratio transform^(27,28,49). Spearman's rhovalues were converted to P values and corrected by theBenjamini-Hochberg procedure⁵² using the cor.test function in R. Thisapproach is conceptually similar to that adopted by SPARCC²⁶, butcalculates the correlation between the OTU abundances and continuousmetadata variables. Heatmaps of correlation p values were constructedusing the heatmap.2 function in R with complete linkage hierarchicalclustering and Euclidean distances.”

Odds ratios of metabolites to identify Nugent BV from Normal werecalculated from conditional logistic regressions performed on allmetabolites using the glm function in R with 10 000 iterations and abinomial distribution. Metabolites with 95% CI>1 and p<0.01 (unpairedt-test, Benjamini-Hochberg corrected) were determined to besignificantly elevated in Nugent BV. “Nugent BV” was defined by theclinical definition of a score of 7-10, with a score of 0-3 being“Nugent Normal”. ROC curves and forest plots were built in R using thepROC and Gmisc packages respectively.

Sample Preparation LC-MS

To confirm GC-MS findings, samples which had at least 100 μL remainingafter GC-MS were also analyzed by LC-MS. 100 μL of supernatant wastransferred to vials with microinserts and directly injected into anAgilent 1290 Infinity HPLC coupled to a Q-Exactive mass spectrometer(Thermo Fisher Scientific) with a HESI source. For HPLC, 2 μL of eachsample was injected into a ZORBAX Eclipse plus C18 2.1×50 mm×1.6 microncolumn. Mobile phase (A) consisted of 0.1% formic acid in water andmobile phase (B) consisted of 0.1% formic acid in acetonitrile. Theinitial composition of 100% (A) was held constant for 30 s and decreasedto 0% over 3.0 min. Mobile phase A was then held at 0% for 1.5 minutesand returned to 100% over 30s for a total run time of 5 min.

Full MS scanning between the ranges of m/z 50-750 was performed on allsamples in both positive in negative mode at 140 000 resolution. TheHESI source was operated under the following conditions: nitrogen flowof 25 and 15 arbitrary units for the sheath and auxiliary gasrespectively, probe temperature and capillary temperature of 425° C. and260° C. respectively and spray voltage of 4.8 kV and 3.9 kV in positiveand negative mode respectively. The AGC target and maximum injectiontime were 3e6 and 500 ms respectively. For molecular characterization,every tenth sample was also analyzed with a data dependent MS² methodwhere a 35 000 resolution full MS scan identified the top 10 signalsabove a 8.3e4 threshold which were subsequently selected at a 1.2 m/zisolation window for MS². Collision energy for MS² was 24, resolution 17500, AGC target 1e5 and maximum injection time was 60ms. Blanks of puremethanol were run between every sample to limit carryover, and a singlesample was run multiple times with every batch to account for anymachine inconsistency. A blank swab extract was also run as a negativecontrol.

For increased sensitivity, a separate LC-MS method was used for relativequantification of GHB in human samples. This was accomplished byselected ion monitoring in the mass range of 103.1-107.1 m/z in positivemode, and integrating the LC peak area of the [M+H⁺] ion (±5 ppm).

Data Processing LC-MS

After data acquisition Thermo .RAW files were converted to .MZML formatusing ProteoWizard (43) and imported into MZmine 2.11 (44)(http://mzmine.sourceforge.net) for chromatogram alignment anddeconvolution. Masses were detected using the Exact Mass setting and athreshold of 1E5. For Chromatogram Builder, minimum time was 0.05 min,minimum height 3E3, and m/z threshold set to 0.025 m/z or 8 ppm.Chromatogram Deconvolution was achieved using the Noise Amplitudesetting with the noise set to 5E4 and signal to 1E5 for negative mode.Due to an overall greater signal and noise in positive mode, the noisewas adjusted to 6E5 and signal to 6.5E5 for positive mode. Join alignerwas used to combine deconvoluted chromatograms into a single file withthe m/z threshold set to 0.05 m/z or 10 ppm, weight for m/z and RT setto 20 and 10 respectively, and a RT tolerance of 0.4 min. Afterchromatograms were aligned, a single .CSV file was exported and allfurther analysis was carried out in R.

To confirm metabolites identified as significant by GC-MS in the LC-MSdata set, the masses of metabolites of interest were searched in theLC-MS data set, and identities confirmed by MS² using METLIN (45) andthe Human Metabolome Database (46) online resources. Standards ofmetabolites of interest were also run to confirm identities whenavailable. An unpaired t-test with Benjamini-Hochberg correction wasused to determine metabolites significantly different between Nugent BVand Normal in the LC-MS data set. Metabolites with corrected p<0.05 wereconsidered statistically significant. Metabolites detected exclusivelyby LC-MS that have previously been associated with BV or health(lactate, trimethylamine) were also included in this analysis. Data waslog base 10 transformed prior to data analysis and zeros replaced by ⅔the minimum detected value on a per metabolite basis. To determineoptimal cut points of biomarkers for diagnostic purposes, cut pointswere computed from LC-MS data using the OptimalCutpoints package in R(47) and the Youden Index method (48).

Validation in Blinded Replication Cohort

Women between the ages of 18 and 40 were recruited from an antenatalclinic at the Nyerere Dispensary in Mwanza, Tanzania as part of a largerstudy on the effect of micronutrient supplemented probiotic yogurt onpregnancy. The study was approved by both the Medical ResearchCoordinating Committee of the National Institute for Medical Research(NIMR), as well as from the Health Sciences Research Ethics Board atWestern University. The study was registered with clinicaltrials.gov(NCT02021799). Samples were collected using the methods mentioned above,and Nugent scores performed by research technicians at NIMR in Mwanza,Tanzania. A subset of samples was selected based on these Nugent scoresby a third party, who ensured there was not repeated sampling of anywomen. Amy McMillan, who performed metabolite analysis, was blinded tothe Nugent scores for the duration of sample processing and dataanalysis. Biomarkers were quantified in samples by LC-MS using theprotocols mentioned above. The study was unblinded after the submissionof BV status based on the ratio cut points established in the Rwandandata set.

Identification of Putative GHB Dehydrogenases in G. vaginalis Strains

The protein sequence of a bona fide 4-hydroxybutyrate (GHB)dehydrogenase isolated from Clostridium kluyveri (25) (GI:347073) wasblasted against all strains of G. vaginalis in the NCBI proteindatabase. Blast results identified multiple isolates containing aputative protein with 44-46% identity to the GHB dehydrogenase from C.kluyveri. The strain used for in vitro experiments (G. vaginalis ATCC14018) was not present in the NCBI protein database, however anucleotide sequence in 14018 with 100% nucleotide identity to a putative4-hydroxybutyrate dehydrogenases in strain ATCC 14019 (GI:311114893) wasidentified, indicating potential for GHB production by strain 14018.

In Vitro Extraction of GHB from Vaginal Isolates

Due to their fastidious nature, we found it difficult to obtainconsistent growth of all vaginal strains in liquid media. To circumventthis, a lawn of bacteria was plated and metabolites were extracted fromagar punches. All strains were grown on Columbia Blood Agar (CBA) platesusing 5% sheep's blood for 96 h under strict anaerobic conditions, withthe exception of L. crispatus, which was grown on de Man Rogosa Sharp(MRS) agar for 48 h. To extract metabolites, 16 agar punches 5 mm indiameter were taken from each plate and suspended in 3 mL 1:1 Me:H₂0.Samples were then sonicated in a water bath sonicater for 1 h,transferred to 1.5 ml tubes after vortexing and spun in a desktopmicrocentrifuge for 10 min at 10 621 g to pellet cells. 200 μl ofsupernatant was then aliquoted for GC-MS described above. The area ofeach peak was integrated using ChemStation (Agilent) by selecting m/z233 in the range of 14-16 min. Initial peak width was set to 0.042 andinitial threshold at 10. An authentic standard of GHB was run withsamples to confirm identification. Un-inoculated media was used as acontrol and experiments were repeated three times with technicalduplicates.

Results

The Vaginal Metabolome is Most Correlated with Bacterial Diversity

We completed a comprehensive untargeted metabolomic analysis of vaginalfluid in two cross-sectional cohorts of Rwandan women: pregnant (P,n=67) and non-pregnant (NP, n=64). To normalize the amount of samplecollected, vaginal swabs were weighed prior to and after collection andnormalized to equivalent concentrations. This enabled us to collectprecise measurements of metabolites in vaginal fluid. Metaboliteprofiling was carried out using both gas chromatography-massspectrometry (GC-MS) and liquid chromatography-mass spectrometry(LC-MS), and microbiota composition by 16S rRNA gene sequencing. Themetabolome determined by GC-MS contained 128 metabolites. We conducted aseries of partial least squares (PLS) regression analyses to determinethe single variable that could best explain the variation in themetabolome. In both cohorts, the diversity of the microbiota, asmeasured using Shannon's Diversity (19), was the factor that explainedthe largest percent variation in the metabolome (Table 1), demonstratingthat the vaginal metabolome is most correlated with bacterial diversity(FIG. 1A). Metabolites robustly associated with this diversity (95%CI<>0) (FIG. 1B) were determined by jackknifing, and within this group,metabolites associated with extreme diversity tended to have lessvariation in the jackknife replicates, and were common to both pregnantand non-pregnant women. This identified a core set of metabolitesassociated with diversity.

The two cohorts overlapped by principal component analysis (PCA) (FIG.7), and nometabolites were significantly different between pregnant andnon-pregnant women (unpaired t-test, Benjamini-Hochberg p>0.01). Thus,the cohorts were combined for all further analysis.

Metabolites and Taxa Associated with Diversity

A single PLS regression was performed on all samples with Shannon'sdiversity as a continuous latent variable (FIG. 8). Samples were thenordered by their position on the 1^(st) component of this PLS. Thediversity indices, microbiota and metabolites associated with diversityof PLS ordered samples are shown in FIG. 2. The vaginal microbiotas ofRwandan women were similar to women from other parts of the world, withthe most abundant species being L. iners followed by L. crispatus(1-3,20) (FIG. 2B). Women with high bacterial diversity were dominatedby a mixture of anaerobes, including Gardnerella, Prevotella, Sneathia,Atopobium, Dialister and Megasphaera species.

FIG. 2D displays metabolites robustly associated with bacterialdiversity in both cohorts based on the PLS loadings in FIG. 1B.Metabolites associated with high diversity include amines, whichcontribute to malodor (16-18), and a number of organic acid derivativessuch as 2-hydroxyisovalerate (2HV), γ-hydroxybutyrate (GHB),2-hydroxyglutarate and 2-hydroxyisocaproate. Low diversity wascharacterized by elevated amino acids, including the amine precursorslysine, ornithine and tyrosine. Many of these metabolites were detectedby LC-MS, and trimethylamine (high diversity) and lactate (lowdiversity) were detected exclusively by this method. The identities ofmetabolites of interest were confirmed with authentic standards whenavailable (FIG. 2, asterisks).

Succinate is Not Associated with Diversity or Clinical BV

Succinate and lactate abundance are shown in panel E of FIG. 2.Succinate levels, and the succinate:lactate ratio have historically beenassociated with BV (21-23), and succinate has been postulated to play animmunomodulatory role (23). Here we show that succinate is notassociated with bacterial diversity, nor is it significantly elevated inclinical BV as defined by Nugent scoring. This trend was independent ofthe detection method used. In addition, succinate was elevated in womendominated by L. crispatus compared with L. iners (unpaired t-test,Benjamini-Hochberg p<0.01) (FIG. 9), indicating L. crispatus may producesuccinate in vivo, a phenomenon that has been demonstrated in vitro(24).

Metabolites Associated with Diversity are Sensitive and Specific forClinical BV

We defined clinical BV by the Nugent method, which is the current goldstandard for BV diagnosis. This microscopy-based technique defines BV asa score of 7-10 when low numbers of lactobacilli morphotypes areobserved, and high numbers of short rods presumed to represent BVassociated bacteria are present. Nugent Normal (N) is defined as a scoreof 1-3, indicating almost exclusively Lactobacillus morphotypes.Intermediate samples are given a score of 4-6 and do not fit into eithergroup. Although Nugent scores correlated well with bacterial diversityin our study, it was apparent from the microbiota and metabolomeprofiles that two samples (41 and 145) had been misclassified by Nugent(FIG. 2A, red dots). The Nugent status of these samples was thereforecorrected prior to all further analyses.

In total we identified 49 metabolites that were significantly differentbetween BV and N (unpaired t-test, Benjamini-Hochberg p<0.01. Nineteenof these have not been reported as differential in the literature, and12 could not be identified. We determined the odds ratio (OR) for BVbased on conditional logistic regressions of all individual metabolitesdetected by GC-MS to determine if the metabolites we associated withhigh bacterial diversity could accurately identify clinical BV asdefined by Nugent scoring. Metabolites significantly elevated in NugentBV (unpaired t-test, Benjamini-Hochberg p<0.01) with OR>1 are shown inFIG. 3A. Succinate was included as a comparator, although it did notreach significance. Both GHB and 2HV were significantly higher in womenwith BV, and had OR>2.0, demonstrating they are novel indicators notonly of high bacterial diversity, but also clinical BV. Receiveroperating characteristics (ROC) curves built from LC-MS data determinedthat high 2HV, high GHB, low lactate and low tyrosine were the mostsensitive and specific biomarkers for BV, with the largest area underthe curve (AUC) achieved using the ratio of 2HV:tyrosine (AUC=0.993)(FIG. 3B-D). ROC curves of GC-MS data identified similar trends, withthe largest AUC achieved by the ratio of GHB:tyrosine (AUC=0.968) (Table2).

We determined the optimal cut points for the GHB:tyrosine (0.621) and2HV:tyrosine (0.882) ratios by selecting values which maximized thesensitivity and specificity for BV. Nugent intermediate samples groupedequally with N or BV based on these cut points, and intermediate-scoredsamples with smaller proportions of lactobacilli tended to group with BV(FIG. 4).

Validation of Biomarkers in a Blinded Replication Cohort from Tanzania

We validated these biomarkers in a blinded cohort of 45 pregnant womenfrom Mwanza, Tanzania (Bisanz at al, manuscript submitted). Using the2HV:tyrosine cut point identified in the Rwanda data set, we identifiedNugent BV with 89% sensitivity and 94% specificity in the validation set(AUC=0.946), demonstrating our findings are reproducible in anethnically distinct population (FIG. 5). The GHB:tyrosine ratio cutpointwas slightly less specific (88%), with an AUC of 0.948. We confirmedthat succinate was not significantly different between Nugent N and BVin the validation set, nor was the succinate:lactate ratio.

Identification of G. vaginalis as a Producer of GHB

Correlations between metabolites and the OTU abundances were performedusing a method that took into account both the compositional nature of16S rRNA gene survey data and the technical variation^(40, 49, 50).Metabolites and taxa which contained any correlation below aBenjamini-Hochberg corrected p<0.01 are displayed as a heatmap in FIG.10. Correlations between metabolites and all taxa indicated thattyramine, putrescine, and cadaverine were most correlated with Dialister(Pearson's R=0.53, 0.58, 0.69, p<0.01) (Table 3), indicating this genusmay contribute to malodor. We found that GHB was most correlated with G.vaginalis (Pearson's R=0.66, p<0.01), while 2HV was most correlated withDialister, Prevotella, and Atopobium (Pearson's R=0.61, 0.58, 0.55,p<0.01).

We chose to investigate the correlation between GHB and G. vaginalis,since this metabolite was novel, and predictive for both Shannon'sdiversity and Nugent BV. Examination of available genomes showed thatmany strains of G. vaginalis possess a putative GHB dehydrogenase(annotated as 4-hydroxybutyrate dehydrogenase). We extracted metabolitesfrom bacterial colonies grown on agar plates and reproducibly detectedGHB in G.vaginalis extracts well above control levels (unpaired t-test,p<0.05), but did not detect GHB from other species commonly associatedwith BV (FIG. 6, Table 4). These data suggest that G.vaginalis is theprimary source of GHB detected in vivo.

TABLE 1 Percentage of variation in the metabolome that can be explainedby a given variable. The percent variation explained by the x-axis(Component 1) of PLS regression plots are shown, where each variable wasused as an independent continuous latent variable. Pregnant Non-PregnantVariable Comp1[%] Comp1[%] Shannon's 9.081732 10.668774 Diversity Nugent6.659424 10.235316 pH 6.089376 9.010365 Lactobacillus 6.985202 10.048794Gardnerella 4.233945 4.41815541 Prevotella 6.69468 7.938502 Atopobium5.324604 6.107562 Dialister 8.610902 6.247007 Megasphaera 3.720202324.61373 Sample ID 5.29943535 6.480972

TABLE 2 ROC area under the curve (AUC) of GC-MS detected metabolites toidentify Nugent BV from N. metabolite or ratio AUC GHB/tyrosine 0.9682HV/tyrosine 0.947 high 2HV 0.894 high GHB 0.893 low tyrosine 0.865 highsuccinate 0.61

TABLE 3 Correlation between metabolites of interest and bacterial taxa.2HV pearson.ecor pearson.ep pearson.eBH spearman.erho spearman.epspearman.eBH Firmicutes; Negativicutes; 0.60600537 3.30E−14 1.68E−120.5558418 2.49E−12 1.27E−10 Selenomonadales; Veillonellaceae; DialisterBacteroidetes; Bacteroidia; Bacteroidales; 0.58520118 3.43E−13 8.76E−120.47777176 1.89E−08 3.91E−07 Prevotellaceae; Prevotella Actinobacteria;Actinobacteria; 0.55115247 1.30E−11 2.20E−10 0.40488614 2.41E−062.82E−05 Coriobacteriales; Coriobacteriaceae; Atopobium Firmicutes;Clostridia; Clostridiales; 0.45094202 1.66E−07 1.42E−06 0.322171560.00030583 0.00134817 Clostridiales_Family_XI_Incertae_Sedis(Peptostreptococcaceae); Parvimonas (Peptostreptococcus) Actinobacteria;Actinobacteria; 0.44166422 1.57E−07 1.40E−06 0.46946982 3.01E−085.42E−07 Bifidobacteriales; Bifidobacteriaceae; Gardnerella Firmicutes;Negativicutes; 0.39089983 4.68E−06 2.94E−05 0.3023744 0.000527120.00211323 Selenomonadales; Veillonellaceae; Megasphaera Firmicutes;Negativicutes; 0.37174236 2.51E−05 0.00010284 0.33700568 0.00013130.00071769 Selenomonadales; Veillonellaceae; Veillonellaceae Firmicutes;Clostridia; Clostridiales; 0.37007616 2.36E−05 0.00010372 0.299974440.00074616 0.00281363 Peptococcaceae2; Desulfotomaculum Fusobacteria;Fusobacteria; Fusobacteriales; 0.3536948 3.76E−05 0.00015414 0.126907350.15108217 0.24478806 Leptotrichiaceae; Sneathia Fusobacteria;Fusobacteria; Fusobacteriales; 0.2708063 0.00241089 0.005741130.09456653 0.300704 0.40623705 Leptotrichiaceae; LeptotrichiaBacteroidetes; Bacteroidia; Bacteroidales; 0.26170159 0.003238780.00748742 0.14766672 0.10111267 0.17484295 Porphyromonadaceae;Porphyromonas Bacteroidetes; Bacteroidia; Bacteroidales; 0.252819140.00910214 0.01647803 0.20323819 0.03023195 0.06363619Porphyromonadaceae; Porphyromonadaceae Fusobacteria; Fusobacteria;Fusobacteriales; 0.2516436 0.00396966 0.00914982 0.05551792 0.531642730.61641743 Leptotrichiaceae; unclassified Firmicutes; Clostridia;Clostridiales; 0.23179266 0.00849663 0.01692367 0.23958014 0.006800740.02031086 Clostridiales_IncertaeSedisXI; Peptoniphilus Firmicutes;Clostridia; Cloridiales; 0.22872429 0.01263764 0.02312852 0.203205740.03237024 0.06687424 Lachnospiraceae; Butyrivibrio Firmicutes;Clostridia; Clostridiales; 0.20170051 0.02563534 0.04283972 0.149919390.09932141 0.17179193 Clostridiales_IncertaeSedisXI; unclassifiedFirmicutes; Lactobacillales; 0.1909414 0.03160352 0.0519773 0.184649790.03985809 0.08313279 Carnobacteriaceae (Aerococcaceae); Granulicatella(Aerococcaceae); unclassified Firmicutes; Clostridia; Clostridiales;0.16271101 0.09812866 0.13314892 0.08837221 0.34523292 0.44094065Lachnospiraceae; Moryella Firmicutes; Clostridia; Clostridiales;0.15466494 0.08167386 0.11854945 0.09094375 0.30933594 0.41958272unclassified; BVAB1_2_3 Firmicutes; Clostridia; Clostridiales; 0.14335220.11140973 0.15506989 0.04820462 0.59347812 0.66109539Peptostreptococcaceae; Peptostreptococcus Firmicutes; Negativicutes;Selenomonadales; 0.10012567 0.26056142 0.33133115 −0.0576918 0.519376680.60701457 Veillonellaceae; Veillonella Actinobacteria; Actinobacteria;0.04472732 0.61348571 0.69187443 −0.0340811 0.70090314 0.74633715Bifidobacteriales; Bifidobacteriaceae; unclassified Firmicutes; Bacilli;Bacillales; 0.02750373 0.60754259 0.67194214 0.01901891 0.638009340.6959755 Bacillaceae2; unclassified Fusobacteria; Fusobacteria;Fusobacteriales; 0.00230961 0.74188668 0.79375836 −0.0660609 0.465542190.55875019 Fusobacteriaceae; Fusobacterium Bacteroidetes; Bacteroidia;Bacteroidales; −0.0015177 0.83917508 0.874238 −0.0752092 0.415198910.51194294 Prevotellaceae; unclassified Actinobacteria; Actinobacteria;−0.0052748 0.70824909 0.7656668 −0.0159685 0.7259052 0.76495548Actinomycetales; Actinomycetaceae; Mobiluncus Firmicutes; Clostridia;Clostridiales; −0.0108045 0.87674243 0.90692694 0.03710047 0.677706140.73135001 Clostridiales_IncertaeSedisXI; Anaerococcus seq_57; Other;Other; Other; Other −0.0178778 0.66164055 0.71730195 −0.07675380.41447985 0.50132278 Bacteriodetes; Bacterodia; Bacteroidales;−0.0381615 0.6039972 0.66268255 −0.0722071 0.4531136 0.53497274Bacteroidaceae; Bacteroides TM7; unclassified; unclassified; −0.04139760.5922482 0.64996375 0.0565856 0.53065842 0.60316577 unclassified; TM7Firmicutes; Clostridia; Clostridiales; −0.0521789 0.56297081 0.63134146−0.0575071 0.52714164 0.60686547 Lachnospiraceae; unclassifiedTenericutes; Mollicutes; Mycoplasmatales; −0.1020763 0.298416060.36222553 −0.058314 0.52400508 0.60401022 Mycoplasmataceae; MycoplasmaFirmicutes; Bacilli; Lactobacillales; −0.116099 0.23111984 0.28990062−0.1368121 0.14732187 0.22808267 Enterococcaceae; unclassifiedFirmicutes; Clostridia; Clostridiales; −0.1562279 0.09318734 0.13003396−0.1076125 0.23313946 0.336017 Clostridiales_IncertaeSedisXI; FinegoldiaFirmicutes; Clostridia; Clostridiales; −0.163697 0.10159311 0.13726306−0.1242001 0.19171111 0.27875721 unclassified; ClostridialesProteobacteria; Gammaproteobacteria; −0.1712075 0.12770933 0.16355187−0.1476057 0.18714704 0.24966143 Enterobacteriales; Enterobacteriaceae;Proteus Firmicutes; Clostridia; Clostridiales; −0.1729021 0.090802870.12363059 −0.1386541 0.16186543 0.23799918 Lachnospiraceae; RoseburiaFirmicutes; Clostridia; Clostridiales; −0.1895158 0.06208835 0.08741935−0.2033432 0.03159267 0.06541593 Ruminococcaceae; FaecalibacteriumFirmicutes; Bacilli; Bacillales; −0.1974775 0.06413735 0.08890124−0.1503821 0.13368065 0.2034887 Paenibacillaceae1; PaenibacillusBacteroidetes; Bacteroidia; Bacteroidales; −0.2080327 0.027892320.04441215 −0.2374581 0.00876974 0.02407996 Prevotellaceae;Prevotellaceae Actinobacteria; Actinobacteria; −0.2266473 0.010007710.01926518 −0.1947268 0.02830751 0.06381711 Bifidobacteriales;Bifidobacteriaceae; Bifidobacterium Bacteroidetes; Bacteroidia;Bacteroidales; −0.2357637 0.022893 0.03536374 −0.2297873 0.016367840.03746771 Bacteroidaceae; Bacteroides Firmicutes; Bacilli;Lactobacillales; −0.2522641 0.00394012 0.00891408 −0.1543695 0.081154390.1481308 Streptococcaceae; Streptococcus Proteobacteria;Gammaproteobacteria; −0.2958338 0.0014534 0.00356496 −0.32084590.00029098 0.0012858 Enterobacteriales; Enterobacteriaceae;Escherichia/Shigella Firmicutes; Bacilli; Lactobacillales; −0.31151150.00030724 0.00096953 −0.1956684 0.02603087 0.05983535 Lactobacillaceae;Lactobacillus_gasseri/johnsonii Firmicutes; Bacilli; Lactobacillales;−0.3139858 0.00027365 0.00088583 −0.2082727 0.01766795 0.04491276Lactobacillaceae; Lactobacillus_jensenii Actinobacteria; Actinobacteria;−0.3387363 0.00010832 0.00038102 −0.3188516 0.00028539 0.00129783Actinomycetales; Corynebacteriaceae; Corynebacterium Proteobacteria;Gammaproteobacteria; −0.3429449 0.00037438 0.00098664 −0.38111721.50E−05 0.00011986 Enterobacteriales; Enterobacteriaceae; unclassifiedFirmicutes; Bacilli; Lactobacillales; −0.3600745 2.62E−05 0.00011595−0.305816 0.00043474 0.00178995 Lactobacillaceae; Lactobacillus_inersFirmicutes; Bacilli; Lactobacillales; −0.3831506 6.65E−06 3.95E−05−0.3248793 0.00017464 0.00094937 Lactobacillaceae;Lactobacillus_crispatus Firmicutes; Bacilli; Lactobacillales; −0.49136172.99E−09 3.78E−08 −0.379802 9.90E−06 9.07E−05 Lactobacillaceae;Lactobacillus

TABLE 4 Bacterial strains used for in vitro experiments. Strain SourceL. iners AB-1 isolated from the vagina of a healthy woman L. crispatus33820 ATCC P. bivia ATCC 29303 ATCC G. vaginalis 14018 ATCC M. curtisii35241 ATCC A. vaginae isolated from the vagina of a woman with BV

Discussion

The present invention demonstrates that the vaginal metabolome isstrongly correlated with bacterial diversity in both pregnant andnon-pregnant Rwandan women, and identified 2HV and GHB as novelbiomarkers of clinical BV, the latter of which we attribute toproduction by G. vaginalis. We obtained extremely accurate results bycontrolling for the mass of vaginal fluid collected, however werecognize this may not be logistically possible in a clinical setting.To circumvent this need we expressed biomarkers as ratios to the aminoacid tyrosine, the most differential amino acid in health. Given thehighly conserved nature of the vaginal microbiota across differentpopulations and ethnicities (1-3, 20), we expect these biomarkers to beglobally applicable for the diagnosis of BV, and our ability toreplicate findings in a distinct population strongly supports thistheory.

The finding that succinate, an end product of anaerobic respiration, wasnot significantly elevated in women with BV was an unexpected outcome.This metabolite has historically been associated with the condition, buthas not been tested in the context of a large untargeted metabolomicstudy. Other groups have reported large ranges in succinate abundance inwomen with BV (21,22), or used pooled samples (22).

In addition to GHB, 2HV was identified as a highly specific novelbiomarker for BV. 2HVis produced from breakdown of branched chain aminoacids in humans (28) and some bacteria (29-31). When the trend for aminoacid depletion in BV is considered, these findings suggest increasedamino acid catabolism in this condition. Some of these amino acids areconverted to the amines cadaverine, tyramine, and putrescine, which arealso associated with BV. These odor-causing compounds were mostcorrelated with Dialister. Yeoman et al. (32) also linked amines toDialister species, and the decarboxylating genes required for amineproduction are expressed by this genus in vivo (27).These data stronglysuggest that Dialister is one of the genera responsible for malodor inthe vagina. Given the small proportion of this genus in women with BV(0.2-8% in our study), this emphasizes the need for functionalcharacterizations of the microbiome using metabolomic and transcriptomicapproaches.

Using an untargeted metabolomics approach, we identify novel biomarkersfor BV in a cohort of 131 Rwandan women, and demonstrate that metabolicproducts in the vagina are closely associated with bacterial diversity.Metabolites associated with high diversity and clinical BV includes2-hydroxyisovalerate and γ-hydroxybutyrate (GHB), but not the anaerobicend-product succinate, while low diversity is characterized by lactateand amino acids. These biomarkers are independent of pregnancy status,and were validated in a blinded replication cohort from Tanzania (n=45),in which we predicted clinical BV with 91% accuracy. Correlationsbetween the metabolome and microbiota identified Gardnerella vaginalisas a putative producer of one of these compounds, GHB, and wedemonstrate production by this species in vitro. This work providesnovel insight into the metabolism of the vaginal microbiota andidentifies highly specific biomarkers for a common condition.

REFERENCES

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Through the embodiments that are illustrated and described, thecurrently contemplated best mode of making and using the invention isdescribed. Without further elaboration, it is believed that one ofordinary skill in the art can, based on the description presentedherein, utilize the present invention to the full extent. Allpublications cited herein are incorporated by reference.

Although the description above contains many specificities, these shouldnot be construed as limiting the scope of the invention, but as merelyproviding illustrations of some of the presently embodiments of thisinvention.

What is claimed is:
 1. A method of diagnosing bacterial vaginosis (BV)in a female subject comprising: (a) obtaining an appropriate sample fromthe subject; and (b) detecting the presence of at least one of2-hydroxyisovalerate (2HV) and γ-hydroxybutyrate (GHB) in the sample,wherein the presence of at least one of 2HV and GHB indicates BVdiagnosis in the subject.
 2. The method of claim 1, wherein a ratio ofat least one of 2HV and GHB to another metabolite in the sample iscalculated, and wherein said diagnosis is based on the ratio.
 3. Themethod of claim 1 or 2, the presence of 2HV or GHB is detected usinghigh performance liquid chromatography, thin layer chromatography (TLC),electrochemical analysis, Mass Spectroscopy (MS), refractive indexspectroscopy (RI), Ultra-Violet spectroscopy (UV), fluorescent analysis,radiochemical analysis, Near-InfraRed spectroscopy (Near-IR), NuclearMagnetic Resonance spectroscopy (NMR), fluorescence spectroscopy, dualpolarisation interferometry, computational methods, Light Scatteringanalysis (LS), gas chromatography (GC), or GC coupled with MS (GC-MS),direct injection (DI) coupled with LC-MS/MS.
 4. The method of claim 1,wherein the presence of 2HV or GHB is detected using GC-MS, the ratio ofat least one of 2HV and GHB to tyrosine in the sample is calculated, andwherein said diagnosis is based on the ratio.
 5. The method of claim 4,wherein the ratio of GHB to tyrosine is 0.6 or above for BV diagnosis.6. The method of claim 4, wherein the ratio of 2-HV to tyrosine is 0.8or above for BV diagnosis.
 7. The method according to any one of claims1-6, wherein the method does not rely on the presence of succinate inthe sample.
 8. A method of diagnosing bacterial vaginosis (BV) in afemale subject comprising: (a) obtaining an appropriate sample from thesubject; and (b) obtaining a level of at least one metabolite in thesample and comparing the level of the at least one metabolite in thesample to the level of said at least one metabolite in a known normalsample (control sample), wherein the presence of the at least onemetabolite in relatively higher levels than in the normal sampleindicates BV diagnosis in the subject, and wherein the metabolite isselected from the group consisting of 2-hydroxyisovalerate (2HV),γ-hydroxybutyrate (GHB), methyl phosphate, 2-hydroxyglutarate,5-aminovalerate, 2-hydroxyisocaproate, 2-hydroxy-3-methylvalerate,mannose-6-phosphate, 2-O-glycerol-d-galactopyranoside, beta-alanine,phenylethylamine and n-acetyl-putrescine.
 9. The method of claim 8,wherein a ratio of the level of the at least one metabolite to the levelof another metabolite in the sample is calculated, and wherein saiddiagnosis is based on the ratio.
 10. The method of claim 8 or 9, whereinthe level of the at least one metabolite is detected using highperformance liquid chromatography, thin layer chromatography (TLC),electrochemical analysis, Mass Spectroscopy (MS), refractive indexspectroscopy (RI), Ultra-Violet spectroscopy (UV), fluorescent analysis,radiochemical analysis, Near-InfraRed spectroscopy (Near-IR), NuclearMagnetic Resonance spectroscopy (NMR), fluorescence spectroscopy, dualpolarisation interferometry, computational methods, Light Scatteringanalysis (LS), gas chromatography (GC), or GC coupled with MS (GC-MS),direct injection (DI) coupled with LC-MS/MS.
 11. The method according toany one of claims 8-10, wherein the method does not rely on the level ofsuccinate in the sample.
 12. The method according to any one of claims1-11, wherein the sample is a sample of vaginal fluid.
 13. The method ofclaims 1-12, wherein the method further comprises specific pathogenicbacterial quantification.
 14. The method of claim 13, wherein thespecific pathogenic bacteria is selected from the group consisting of G.vaginalis and bacteria of the genera Dialister, Prevotella andAtopobium.
 15. A method of treating bacterial vaginosis (BV) in apatient, the method comprising obtaining a metabolic profile of thepatient, correlating each metabolite of the metabolic profile with abacterium, and administering the patient a drug or drugs that areeffective against the correlated bacterium.
 16. The method of claim 15,wherein the metabolic profile includes γ-hydroxybutyrate (GHB), andwherein drug or drugs are effective against G. vaginalis.
 17. The methodof claim 15, wherein the metabolic profile includes 2-hydroxyisovalerate(2HV), and wherein the drug or drugs are effective against bacteria ofthe genera Dialister, Prevotella and Atopobium.
 18. A method ofdetermining the efficacy of a bacterial vaginosis (BV) treatment in apatient undergoing BV treatment, the method comprising: (a) obtaining anappropriate sample of the patient at different stages of the treatment;(b) obtaining the levels of at least one of 2-hydroxyisovalerate (2HV)and γ-hydroxybutyrate (GHB) in the samples, wherein a progressivedecrease in the levels of 2HV and GHB along the stages is indicative ofthe efficacy of the treatment.
 19. The method of claim 18, wherein thelevels of at least one of 2HV and GHB is detected using high performanceliquid chromatography, thin layer chromatography (TLC), electrochemicalanalysis, Mass Spectroscopy (MS), refractive index spectroscopy (RI),Ultra-Violet spectroscopy (UV), fluorescent analysis, radiochemicalanalysis, Near-InfraRed spectroscopy (Near-IR), Nuclear MagneticResonance spectroscopy (NMR), fluorescence spectroscopy, dualpolarisation interferometry, computational methods, Light Scatteringanalysis (LS), gas chromatography (GC), or GC coupled with MS (GC-MS),direct injection (DI) coupled with LC-MS/MS.
 20. The method of claim 19,wherein the ratio of the levels of at least one of 2HV and GHB to thelevel of another metabolite in the sample is calculated, and whereinsaid efficacy is determined on a decrease or an increase in the ratio.21. The method of claim 20, wherein the levels of at least one of 2HVand GHB is detected using GC-MS, the other metabolite is tyrosine, andwherein said efficacy is based on the ratio.
 22. A method of determiningthe efficacy of a bacterial vaginosis (BV) treatment in a patientundergoing BV treatment, the method comprising obtaining an appropriatesample of the patient at different stages of the treatment; (b)obtaining the levels of at least one metabolite selected from the groupconsisting of 2-hydroxyisovalerate (2HV), γ-hydroxybutyrate (GHB),methyl phosphate, 2-hydroxyglutarate, 5-aminovalerate,2-hydroxyisocaproate, 2-hydroxy-3-methylvalerate, mannose-6-phosphate,2-O-glycerol-d-galactopyranoside, beta-alanine, phenylethylamine andn-acetyl-putrescine in the samples, wherein a progressive decrease inthe levels of the at least one metabolite along the different stages isindicative of the efficacy of the treatment.
 23. The method of claim 22,wherein the levels are obtained as a ratio of the at least onemetabolite to the level of another metabolite.
 24. The method of claim23, wherein the other metabolite is tyrosine.
 25. A method of diagnosingbacterial vaginosis (BV) in a subject comprising: (a) obtaining ametabolite profile from the subject; and (b) using multivariatestatistical analysis and machine learning to compare the subject'sprofile with a predetermined set of metabolite profiles of BV and apredetermined set of metabolite profiles of non-BV (referred to as“control” or “normal”) to determine if the subject has BV.
 26. Themethod of claim 25, wherein the subject's metabolite profile and thepredetermined set of metabolite profiles are obtained usingmetabolomics.
 27. The method of claim 26, wherein the metabolomics isperformed with one or more of high performance liquid chromatography,thin layer chromatography, electrochemical analysis, mass spectroscopy(MS), refractive index spectroscopy, ultra-violet spectroscopy,fluorescent analysis, radiochemical analysis, near-infraredspectroscopy, nuclear magnetic resonance (NMR), light scatteringanalysis, gas chromatography (GC), or GC coupled with MS, directinjection (DI) coupled with LC-MS/MS.
 28. The method of claim 25, 26 or27, wherein the steps of the method are executed using a suitablyprogrammed computer.
 29. The method of claim 25, 26, 27 or 28, whereinmetabolite profiles are obtained from a biological sample.
 30. Themethod of claim 25, 26, 27, 28 or 29, wherein the metabolite includes atleast one of 2-hydroxyisovalerate (2HV) and γ-hydroxybutyrate (GHB). 31.The method of claim 25, 26, 27, 28, 29 or 30, wherein the metaboliteinclude 2-hydroxyisovalerate (2HV), γ-hydroxybutyrate (GHB), methylphosphate, 2-hydroxyglutarate, 5-aminovalerate, 2-hydroxyisocaproate,2-hydroxy-3-methylvalerate, mannose-6-phosphate,2-O-glycerol-d-galactopyranoside, beta-alanine, phenylethylamine andn-acetyl-putrescine.
 32. Use of a metabolic profile of a bacterialvaginosis (BV) patient to correlate the metabolic profile with specificbacteria, and to select a drug to treat the patient based on saidspecific bacteria.
 33. The use of G. vaginalis for the production ofγ-hydroxybutyrate (GHB).